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Active Learning and Discovery Package by Timothy Hospedales
Active Learning and Discovery Package V0.1 Timothy Hospedales, 09/2012. http://www.eecs.qmul.ac.uk/~tmh/ Described in: [Hospedales et al. PAKDD'11 / Haines et al. BMVC'11, Hospedales et al. IEEE TKDE'12 and Hospedales et al. ECCV'12]. See batch_experiment.m for an example run of a few UCI datasets. (Note: This should take about 10 minutes to run depending on speed of your PC) Models included: % 1. Random criteria, SVM classifier. % 2. Entropy criteria, SVM classifier. % 3. Likelihood criteria, KDE classifier. % 4. pWrong criteria, KDE classifier. (Haines 2011 BMVC) % 5. Gen/Disc fusion criteria, SVM/KDE classifier. (Hospedales 2012 IEEE TKDE) % 6. DPEA criteria, KDE classifier. (Hospedales 2012 ECCV) Notes: - To run models [1,2,5] you may need to compile libsvm for your platform in ./libsvm/. Everything else is matlab. - Renamed libsvm's svmtrain -> libsvmtrain to avoid conflict with matlab builtin function svmtrain. Data Structure Format: To setup the input for the script, please save the following structures in the input mat file. P.Nx = Number of input dimensions. P.Ny = Number of classes. P.Ntr = Number of train instances. P.Nte = Number of test instances. CV(fold).Xr = Train data. [P.Ntr x P.Nx] CV(fold).Xt = Test data. [P.Ntr x 1]. CV(fold).Ytr = Train labels. CV(fold).Yte = Test labels
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